A Moderate Attribute Reduction Approach in Decision-Theoretic Rough Set

  • Hengrong Ju
  • Xibei Yang
  • Pei Yang
  • Huaxiong Li
  • Xianzhong Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9437)


Attribute reduction is an important topic in Decision-Theoretic Rough Set theory. To overcome the limitations of lower-approximation-monotonicity based reduct and cost minimum based reduct, a moderate attribute reduction approach is proposed in this paper, which combines the lower approximation monotonicity criterion and cost minor criterion. Furthermore, the proposed attribute reduct is searched by solving an optimization problem, and a fusion fitness function is proposed in a generic algorithm, such that the reduct is computed in a low time complexity. Experimental analysis is included to validate the theoretic analysis and quantify the effectiveness of the proposed attribute reduction algorithm. This study indicates that the optimality is not the best and sub-optimum may be the best choice.


Attribute reduction Decision cost DTRS Lower-approximation-monotonicity 



This work is supported by the Natural Science Foundation of China (Nos. 61100116, 71201076, 61170105, 61473157,71171107), Qing Lan Project of Jiangsu Province of China, and the Ph.D. Programs Foundation of Ministry of Education of China (20120091120004).


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Authors and Affiliations

  • Hengrong Ju
    • 1
    • 2
  • Xibei Yang
    • 2
  • Pei Yang
    • 1
    • 3
    • 4
  • Huaxiong Li
    • 1
    • 4
  • Xianzhong Zhou
    • 1
    • 4
  1. 1.School of Management and EngineeringNanjing UniversityNanjingPeople’s Republic of China
  2. 2.School of Computer Science and EngineeringJiangsu University of Science and TechnologyZhenjiangPeople’s Republic of China
  3. 3.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China
  4. 4.Research Center for Novel Technology of Intelligent EquipmentsNanjing UniversityNanjingPeople’s Republic of China

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